Identification of Adulteration in Botanical Samples Supplements with Untargeted Metabolomics

Authors: KELLOGG, J - University Of North Carolina Greensboro; WALLACE, E - University Of North Carolina Greensboro; TODD, D - University Of North Carolina Greensboro; CECH, N - University Of North Carolina Greensboro; Harnly, James - Jim

Submitted to: Analytical and Bioanalytical Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/21/2020
Publication Date: 4/29/2020
Citation: Kellogg, J.J., Wallace, E.D., Todd, D.A., Cech, N.B., Harnly, J.M. 2020. Identification of Adulteration in Botanical Samples Supplements with Untargeted Metabolomics. Analytical and Bioanalytical Chemistry. 41: 4273-4286. https://doi.org/10.1007/s00216-020-02678-6.
DOI: https://doi.org/10.1007/s00216-020-02678-6

Interpretive Summary: Golden Seal samples were analyzed by several untargeted methods to determine their authenticity. Adulteration of botanical supplements is a major concern in the US market. While it is common to employ a targeted analysis to detect known compounds in a sample, this is difficult when the sample material is not well known. In this study, targeted and untargeted analysis, i.e., metabolomics using liquid chromatography coupled to ultraviolet-visible spectroscopy (LC-UV) or high-resolution mass spectrometry (LC-MS), were compared for the detection of adulterated Golden Seal. To evaluate the different analytical approaches, Golden Seal was adulterated with a little known botanical. While the targeted analysis was the most sensitive to detecting adulteration, each of the statistical approaches detected adulteration of the goldenseal samples from the untargeted metabolomics datasets, with SIMCA providing the greatest discriminating potential.

Technical Abstract: Adulteration remains an issue in the dietary supplement industry, including botanical supplements. While it is common to employ a targeted analysis to detect known adulterants, this is difficult when little is known about the sample set. With this study, untargeted metabolomics using liquid chromatography coupled to ultraviolet-visible spectroscopy (LC-UV) or high-resolution mass spectrometry (LC-MS) was employed to detect adulteration in botanical dietary supplements. To evaluate each approach, a training set was prepared by combining Hydrastis canadensis L. with a known adulterant, Coptis chinensis Franch., in ratios ranging from 5% to 95% adulteration. The metabolomics datasets were analyzed using both unsupervised (PCA and composite score) and supervised (SIMCA) techniques. Palmatine, a known H. canadensis metabolite, was quantified to provide a targeted analysis comparison. While the targeted analysis was the most sensitive to detecting adulteration of the methods compared, each of the statistical approaches detected adulteration of the goldenseal samples from the untargeted metabolomics datasets, with SIMCA providing the greatest discriminating potential.